arxiv_cv 95% Match Research Paper Machine Learning Engineers,Computer Vision Researchers,AI Developers in data-scarce domains 17 hours ago

Crucial-Diff: A Unified Diffusion Model for Crucial Image and Annotation Synthesis in Data-scarce Scenarios

generative-ai › diffusion

📄 Abstract

Abstract: The scarcity of data in various scenarios, such as medical, industry and autonomous driving, leads to model overfitting and dataset imbalance, thus hindering effective detection and segmentation performance. Existing studies employ the generative models to synthesize more training samples to mitigate data scarcity. However, these synthetic samples are repetitive or simplistic and fail to provide "crucial information" that targets the downstream model's weaknesses. Additionally, these methods typically require separate training for different objects, leading to computational inefficiencies. To address these issues, we propose Crucial-Diff, a domain-agnostic framework designed to synthesize crucial samples. Our method integrates two key modules. The Scene Agnostic Feature Extractor (SAFE) utilizes a unified feature extractor to capture target information. The Weakness Aware Sample Miner (WASM) generates hard-to-detect samples using feedback from the detection results of downstream model, which is then fused with the output of SAFE module. Together, our Crucial-Diff framework generates diverse, high-quality training data, achieving a pixel-level AP of 83.63% and an F1-MAX of 78.12% on MVTec. On polyp dataset, Crucial-Diff reaches an mIoU of 81.64% and an mDice of 87.69%. Code is publicly available at https://github.com/JJessicaYao/Crucial-diff.

Key Contributions

Introduces Crucial-Diff, a domain-agnostic framework using diffusion models to synthesize 'crucial' training samples that specifically target a downstream model's weaknesses, rather than just generic augmentation. It employs a Scene Agnostic Feature Extractor (SAFE) and a Weakness Aware Sample Miner (WASM) for efficient and targeted sample generation.

Business Value

Significantly improves the performance of AI models in data-scarce domains like medical imaging and autonomous driving by generating more effective training data. This leads to more reliable and accurate AI systems, reducing development costs and time.

Paper Metadata

Innovation Type

Algorithmic Framework

Deployment Feasibility

High, as it's a framework for generating data, which can be integrated into existing training pipelines.

Limitations Addressed

Synthetic samples are often repetitive or simplistic,Existing methods fail to provide 'crucial information' targeting model weaknesses,Separate training required for different objects, leading to inefficiency

Technical Tags

data scarcityimage synthesiscrucial samplesweakness awaredomain-agnosticdiffusion modelsdetectionsegmentationmedical imagingautonomous driving

Research Topics

Generative AIData AugmentationDomain AdaptationComputer VisionDeep Learning

Methods & Architectures

Scene Agnostic Feature Extractor (SAFE)Weakness Aware Sample Miner (WASM)Diffusion models for synthesisFeedback from detection results Diffusion Models

Applications & Tasks

Medical Imaging Autonomous Driving Industrial Inspection Data scarcityModel overfittingDataset imbalanceRepetitive/simplistic synthetic samplesInefficient training for different objects Image synthesis for data augmentationImproving detection and segmentation performanceGenerating 'crucial' or hard-to-detect samples

Related Fields

Generative AIComputer VisionData AugmentationMachine LearningDeep Learning

Keywords

Data ScarcityImage SynthesisData AugmentationDiffusion ModelsWeakly Supervised LearningObject DetectionSemantic SegmentationMedical ImagingAutonomous DrivingDomain AdaptationCrucial SamplesHard Example Mining

Academic Context

#Generative AI#Data Augmentation#Domain Adaptation#Computer Vision#Deep Learning

Commercial Potential

Potential Products

Data augmentation services for AI developmentTools for improving model robustness in low-data regimesSynthetic data generation platforms

Target Industries

HealthcareAutomotiveManufacturingAerospaceRobotics

Use Case Examples

Generating rare disease images to train diagnostic models.Synthesizing challenging driving scenarios (e.g., low visibility, unusual obstacles) to improve autonomous vehicle perception.

Competitive Edge

Goes beyond standard data augmentation by intelligently generating 'crucial' samples that specifically address model weaknesses, leading to more efficient learning and better performance in data-scarce scenarios.

Market Opportunity

Significant market for AI development tools and data augmentation solutions.

Revenue Models

Licensing of the frameworkSaaS for synthetic data generation

Resource Requirements

Compute Needs

High (for training diffusion models and generating samples)

Data Requirements

Existing datasets from target domains (medical, autonomous driving, etc.) to identify model weaknesses.

Deployment Constraints

Computational cost of synthesis,Ensuring generated samples are realistic and relevant,Integration into existing training pipelines

Scalability

Scales with the complexity of the target domain and the number of 'crucial' samples needed.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

Patent Potential

Moderate (Novel framework and synthesis strategy)

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